In the process of forecasting behavior to optimize the performance of the cars in any transportation system (horizontal or vertical), there are many pieces of information necessary concerning the system that should be obtained. The acquisition of detailed information helps the system in fine tuning its optimization based on this extended knowledge.
One piece of information that can be important to the evaluation or prediction of any building behavior is the building's population spread, that is, how many elevator users are present on a floor-by-floor basis in the building. One can easily understand that an executive floor with a population of, for example, ten (10) people would have a smaller demand for elevator service than would a restaurant floor with a population of, for example, two hundred (200) people.
This shows the need for acquiring the population density of the building on a floor-by-floor basis. Knowing this information provides the system with the ability to decide when the floor is full, empty, moderately full, etc. The availability of this information or knowledge permits the system to make corrective or more accurate decisions based on, for example, historically predicted values.
It is believed that, prior to the present invention, there has been no system or procedure that dynamically evaluated the population density of a building. If population was used, it typically was a hard-coded table that was not changed or updated by the system itself. Should any alteration be required, the user or system maintainer had to physically change the numbers, i.e., re-program the numbers, to provide the best "guesstimate" of the values. Such an operationally static system is not as desirable as a dynamic one, which provides updated, accurate information on a "real time" on-going basis within the system itself.